Plotlars

Plotlars is a versatile Rust library that bridges the gap between the powerful Polars data analysis library and visualization backends. It supports two rendering backends: Plotly for interactive HTML-based charts and Plotters for static image output (PNG/SVG). Plotlars simplifies the process of creating visualizations from data frames, allowing developers to focus on data insights rather than the intricacies of plot creation.

Implemented Plots Overview

Plot Types Reference

Motivation

The creation of Plotlars was driven by the need to simplify the process of creating complex plots in Rust, particularly when working with the powerful Polars data manipulation library. Generating visualizations often requires extensive boilerplate code and deep knowledge of both the plotting library and the data structure. This complexity can be a significant hurdle, especially for users who need to focus on analyzing and interpreting data rather than wrestling with intricate plotting logic.

To illustrate this, consider the following example where a scatter plot is created without Plotlars:

use plotly::{
    common::*,
    layout::*,
    Plot,
    Scatter,
};

use polars::prelude::*;

fn main() {
    let dataset = LazyCsvReader::new(PlRefPath::new("data/penguins.csv"))
        .finish().unwrap()
        .select([
            col("species"),
            col("flipper_length_mm").cast(DataType::Int16),
            col("body_mass_g").cast(DataType::Int16),
        ])
        .collect().unwrap();

    let group_column = "species";
    let x = "body_mass_g";
    let y = "flipper_length_mm";

    let groups = dataset
        .column(group_column).unwrap()
        .unique().unwrap();

    let layout = Layout::new()
        .title(Title::with_text("Penguin Flipper Length vs Body Mass"))
        .x_axis(Axis::new().title(Title::with_text("Body Mass (g)")))
        .y_axis(Axis::new().title(Title::with_text("Flipper Length (mm)")))
        .legend(Legend::new().title(Title::with_text("Species")));

    let mut plot = Plot::new();
    plot.set_layout(layout);

    let groups_str = groups.str().unwrap();

    for group in groups_str.into_iter() {
        let group = group.unwrap();

        let data = dataset
            .clone()
            .lazy()
            .filter(col(group_column).eq(lit(group)))
            .collect().unwrap();

        let x = data
            .column(x).unwrap()
            .i16().unwrap()
            .to_vec();

        let y = data
            .column(y).unwrap()
            .i16().unwrap()
            .to_vec();

        let trace = Scatter::default()
            .x(x)
            .y(y)
            .name(group)
            .mode(Mode::Markers)
            .marker(Marker::new().size(10).opacity(0.5));

        plot.add_trace(trace);
    }

    plot.show();
}

In this example, creating a scatter plot involves writing substantial code to manually handle the data and configure the plot, including grouping the data by category and setting up the plot layout.

Now, compare that to the same plot created using Plotlars:

use plotlars::{
    CsvReader,
    ScatterPlot,
    Plot,
    Rgb,
    polars::prelude::*,
};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let dataset = CsvReader::new("data/penguins.csv")
        .finish()?
        .lazy()
        .select([
            col("species"),
            col("flipper_length_mm").cast(DataType::Int16),
            col("body_mass_g").cast(DataType::Int16),
        ])
        .collect()?;

    ScatterPlot::builder()
        .data(&dataset)
        .x("body_mass_g")
        .y("flipper_length_mm")
        .group("species")
        .opacity(0.5)
        .size(12)
        .colors(vec![
            Rgb(178, 34, 34),
            Rgb(65, 105, 225),
            Rgb(255, 140, 0),
        ])
        .plot_title("Penguin Flipper Length vs Body Mass")
        .x_title("Body Mass (g)")
        .y_title("Flipper Length (mm)")
        .legend_title("Species")
        .build()
        .plot();

    Ok(())
}

This is the output:

With Plotlars, the same scatter plot is created with significantly less code. The library abstracts away the complexities of dealing with individual plot components and allows the user to specify high-level plot characteristics. This streamlined approach not only saves time but also reduces the potential for errors and makes the code more readable and maintainable.

Installation

Plotlars requires exactly one backend feature to be enabled:

# Interactive HTML charts (Plotly)
cargo add plotlars --features plotly

# Static image output (Plotters)
cargo add plotlars --features plotters

Optional features for file loading:

# JSON file support
cargo add plotlars --features plotly,format-json

# Excel file support
cargo add plotlars --features plotly,format-excel

Running the examples

Plotlars comes with several ready-to-use demo programs. Examples are prefixed by backend (plotly_ or plotters_) and require the corresponding feature:

# Plotly examples
cargo run --features plotly --example plotly_barplot

# Plotters examples
cargo run --features plotters --example plotters_barplot

Replace plotly_barplot with the example name (without the .rs extension) you want to run.

Features

• Dual backends: Choose between Plotly (interactive HTML) and Plotters (static PNG/SVG)
• Seamless Polars integration: Build plots directly from DataFrames with no manual extraction
• 22 plot types: Bar, line, scatter, box, histogram, heatmap, 3D, geo, polar, and more
• Faceting and subplots: Split data by category or compose multi-plot grids
• File loaders: Read CSV, Parquet, JSON, and Excel files directly into DataFrames
• Error handling: Use try_build for fallible construction with PlotlarsError
• Polars re-export: Access polars via plotlars::polars without adding it to your Cargo.toml

Exporting Plots

Plotters backend

The plotters backend renders directly to static images. Use save to write PNG or SVG files (format is inferred from the extension), or to_svg for an in-memory SVG string:

use plotlars::{CsvReader, ScatterPlot, Plot};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let dataset = CsvReader::new("data/penguins.csv").finish()?;

    let plot = ScatterPlot::builder()
        .data(&dataset)
        .x("body_mass_g")
        .y("flipper_length_mm")
        .plot_title("Penguin Data")
        .build();

    // Save as PNG or SVG (format inferred from extension)
    plot.save("output.png");
    plot.save("output.svg");

    // Or get an SVG string directly
    let svg = plot.to_svg();

    Ok(())
}

Plotly backend

The plotly backend produces interactive HTML charts. To export them as static images (PNG, JPG, WEBP, SVG) via the write_image method, enable one of the export features and install the corresponding WebDriver:

• Default (recommended - uses any available driver):

  cargo add plotlars --features export-default
  

• Chrome/Chromium:

  cargo add plotlars --features export-chrome
  

  Install ChromeDriver: https://chromedriver.chromium.org/

• Firefox:

  cargo add plotlars --features export-firefox
  

  Install GeckoDriver: https://github.com/mozilla/geckodriver/releases

use plotlars::{CsvReader, ScatterPlot, Plot};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let dataset = CsvReader::new("data/penguins.csv").finish()?;

    let plot = ScatterPlot::builder()
        .data(&dataset)
        .x("body_mass_g")
        .y("flipper_length_mm")
        .plot_title("Penguin Data")
        .build();

    // Export as PNG (1200x800 pixels, 2x scale for high DPI)
    plot.write_image("output.png", 1200, 800, 2.0)?;

    Ok(())
}

Plotlars in Jupyter Notebooks

Plotlars seamlessly integrates with Jupyter Notebooks, allowing you to leverage the power of interactive data visualization directly within your notebook environment. This integration is made possible through the use of the evcxr project, which provides a Jupyter kernel for the Rust programming language.

With Polars, evcxr, and Plotlars, data science in Rust leaps to the next level , making powerful data analysis and visualization more accessible and efficient than ever before.

License

This project is licensed under the MIT License. See the LICENSE.txt file for details.

Acknowledgements

• Polars: For providing a fast and efficient data manipulation library.
• Plotly: For interactive visualization capabilities.
• Plotters: For static rendering capabilities.
• Evcxr: For enabling the use of Rust in Jupyter Notebooks.
• Rust Community: For the support and development of an amazing programming language.